{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stellargraph example: Simplified Graph Convolutions (SGC) on the CORA citation dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates the use of `StellarGraph`'s GCN [[2]](#refs), class for training the simplified graph convolution (SGC) model in introduced in [[1]](#refs).\n",
    "\n",
    "We show how to use `StellarGraph` to perform node attribute inference on the Cora citation network using SGC by creating a single layer GCN model with softmax activation.\n",
    "\n",
    "SGC simplifies GCN in the following ways,\n",
    "\n",
    " - It removes the non-linearities in the graph convolutional layers.\n",
    " - It smooths the node input features using powers of the normalized adjacency matrix with self loops (see [[2]](#refs)).\n",
    " - It uses a single softmax layer such that GCN is simplified to logistic regression on smoothed node features.\n",
    "\n",
    "For a graph with $N$ nodes, $F$-dimensional node features and $C$ number of classes, SGC simplifies GCN using the following logistic regression classifier with smoothed features,\n",
    "\n",
    "$\\hat{\\boldsymbol{Y}}_{SGC} = \\mathtt{softmax}(\\boldsymbol{S}^K \\boldsymbol{X}\\; \\boldsymbol{\\Theta})$\n",
    "\n",
    "where $\\hat{\\boldsymbol{Y}}_{SGC} \\in \\mathbb{R}^{N\\times C}$ are the class predictions; $\\boldsymbol{S}^K \\in \\mathbb{R}^{N\\times N}$ is the normalised graph adjacency matrix with self loops raised to the K-th power; $\\boldsymbol{X}\\in \\mathbb{R}^{N\\times F}$ are the node input features; and $\\boldsymbol{\\Theta} \\in \\mathbb{R}^{F\\times C}$ are the classifier's parameters to be learned.\n",
    "\n",
    "<a name=\"refs\"></a>\n",
    "**References**\n",
    "\n",
    "[1] Simplifying Graph Convolutional Networks. F. Wu, T. Zhang, A. H. de Souza Jr., C. Fifty, T. Yu, and K. Q. Weinberger, arXiv: 1902.07153. [link](https://arxiv.org/abs/1902.07153)\n",
    "\n",
    "[2] Semi-Supervised Classification with Graph Convolutional Networks. T. N. Kipf and M. Welling, ICLR 2016. [link](https://arxiv.org/abs/1609.02907)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GCN\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model, regularizers\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the CORA network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the graph from edgelist (in the order `cited-paper` <- `citing-paper`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgelist = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.cites\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=[\"target\", \"source\"],\n",
    ")\n",
    "edgelist[\"label\"] = \"cites\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(Gnx, \"paper\", \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the features and subject for the nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "column_names = feature_names + [\"subject\"]\n",
    "node_data = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.content\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=column_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Case_Based',\n",
       " 'Genetic_Algorithms',\n",
       " 'Neural_Networks',\n",
       " 'Probabilistic_Methods',\n",
       " 'Reinforcement_Learning',\n",
       " 'Rule_Learning',\n",
       " 'Theory'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(node_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "Here we're taking 140 node labels for training, 500 for validation, and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data, train_size=140, test_size=None, stratify=node_data[\"subject\"]\n",
    ")\n",
    "val_data, test_data = model_selection.train_test_split(\n",
    "    test_data, train_size=500, test_size=None, stratify=test_data[\"subject\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Case_Based': 16,\n",
       "         'Theory': 18,\n",
       "         'Reinforcement_Learning': 11,\n",
       "         'Neural_Networks': 42,\n",
       "         'Probabilistic_Methods': 22,\n",
       "         'Rule_Learning': 9,\n",
       "         'Genetic_Algorithms': 22})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict(\"records\"))\n",
    "val_targets = target_encoding.transform(val_data[[\"subject\"]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict(\"records\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the StellarGraph object\n",
    "\n",
    "We have the graph in networkx format and the node features and targets in a Pandas Dataframe. We are going to use these to create a StellarGraph object that is suitable for machine learning on graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(Gnx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 2708, Edges: 5278\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5278]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare node generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. Since SGC is a full-batch model, we use the `FullBatchNodeGenerator` class to feed node features and graph adjacency matrix to the model. \n",
    "\n",
    "For SGC, we need to tell the generator to smooth the node features by some power of the normalised adjacency matric with self loops before multiplying by the model parameters.\n",
    "\n",
    "We achieve this by specifying `model='sgc'` and `k=2`, in this example, to use the SGC method and take the square of the adjacency matrix. For the setting `k=2` we are considering a 2-hop neighbourhood that is equivalent to a 2-layer GCN. We can set `k` larger to consider larger node neighbourhoods but this carries an associated computational penalty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating 2-th power of normalized A...\n"
     ]
    }
   ],
   "source": [
    "generator = FullBatchNodeGenerator(G, method=\"sgc\", k=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_data.index, train_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the SGC model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. For SGC, we use a single hidden layer with output dimensionality equal to the number of classes.\n",
    " * `activations` is the activation function for the output layer. For SGC the output layer is the classification layer and for multi-class classification it should be a `softmax` activation. \n",
    " * Arguments such as `bias` and `dropout` are internal parameters of the model, execute `?GCN` for details. \n",
    " \n",
    "**Note:** The SGC model is a single layer GCN model with `softmax` activation and the full batch generator we created above that smoothes the node features based on the graph structure. So, our SGC model is declared as a `StellarGraph.layer.GCN` model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgc = GCN(\n",
    "    layer_sizes=[train_targets.shape[1]],\n",
    "    generator=generator,\n",
    "    bias=True,\n",
    "    dropout=0.5,\n",
    "    activations=[\"softmax\"],\n",
    "    kernel_regularizer=regularizers.l2(5e-4),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expose the input and output tensors of the SGC model for node prediction,\n",
    "# via GCN.build() method:\n",
    "x_inp, predictions = sgc.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the input tensors `x_inp` and output tensors being the predictions `predictions` from the final dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=predictions)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.2),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_data.index, val_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create callbacks for early stopping (if validation accuracy stops improving) and best model checkpoint saving:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "if not os.path.isdir(\"logs\"):\n",
    "    os.makedirs(\"logs\")\n",
    "es_callback = EarlyStopping(\n",
    "    monitor=\"val_acc\", patience=50\n",
    ")  # patience is the number of epochs to wait before early stopping in case of no further improvement\n",
    "mc_callback = ModelCheckpoint(\n",
    "    \"logs/best_model.h5\", monitor=\"val_acc\", save_best_only=True, save_weights_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen,\n",
    "    epochs=50,\n",
    "    validation_data=val_gen,\n",
    "    verbose=0,\n",
    "    shuffle=False,  # this should be False, since shuffling data means shuffling the whole graph\n",
    "    callbacks=[es_callback, mc_callback],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the training history:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def remove_prefix(text, prefix):\n",
    "    return text[text.startswith(prefix) and len(prefix):]\n",
    "\n",
    "def plot_history(history):\n",
    "    metrics = sorted(set([remove_prefix(m, \"val_\") for m in list(history.history.keys())]))\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        plt.plot(history.history['val_' + m])\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'validation'], loc='best')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QNn8GP38CH99vLV0HQ+9TIW4U9BgBEYkg0vAHVZZAzmbI2w6leVBeAOX51mtZPlSVwrArYfCljZ9DKaW8yGcTQXiQExBKguIJr18iyNsBb1wF4T1h2mvgjoLkk+HsRyFni5UQNn8KS5+D2irrPUFRVkLoMQLC4yB3G2RvguzNUNBA/YO/C1wR1hhH1RXwzgzY9CGcN8v6PKWUakM+mwgigpwA5LviCK9rQlqWD3OmQW01XP32kV/KMX2tZfyd1hf4vvWQ+RNkroTMVbD4n2BqrC/6mL7QcwyMvBZiTrDW3THgCgen6+A5a2tgyZOw8M+w83uY8m844ey2+SUopRQ+nAjCPIkg29mDxKylVhPSt6+3HuFcNx+iezd9Av9AiBtpLcywtlWWQmkOhMWBw695gTj8YOK90PdMePcXMOdyGHEtnP1ncIW1/AKVUqqZfLbVkJ9DCHX5s8+vG1SXwbwbYPsiuOBJSJrQspMGuCEioflJoL5uQ2DmQphwD6x6HZ4bDxk6X4JSyvu8lghEZLaIZIlIk5PNiMhoEakWkaneiqUxEW4n6XSzVjZ9CBPuhRFXt3UYB/kHwhkPww2fWutzpkHhHvviUUr5BG+WCF4CJjd1gIj4AX8FPvdiHI0KD3KyzfSwVgZeCKf9zo4wjpQwFq5+B6rKYN6NUFNtd0RKqU7Ma4nAGPMtkHeUw34JvANkeSuOpkQEBbCtMhJu+hou+S842tGTstgT4IInYNf3sPBRu6NRSnVitn3ziUgccDHwbDOOnSkiqSKSmp2d3WoxhAc5KSirgvhR4B/QaudtNUMvh5HXw+JZsOULu6NRSnVSdt4CPwE8YIypPdqBxpjnjTEpxpiU2NjYVgsg3O1JBO3ZOX+FrkPg3ZlQkGF3NEqpTsjORJACvCEiacBU4BkRuagtA6grERhj2vJjj40zCC57CWoqPfUF7TxxKaU6HNsSgTEm2RiTZIxJAuYBtxlj5rdlDBFBTqpqDKWVNW35sccupg9M+RekL4Ov/mB3NEqpTsZrHcpEZC4wCYgRkQzgIcAJYIx5zlufeyzCPZ3KCsqqCA5s533rBl8KaUvg+39bYyD1O8fuiJRSnYTXvv2MMVcew7HTvRVHUyLcnmEmSqvoERFkRwjH5uw/Q8ZymH8b3P4jhLRefYlSyne1o/aSba9umIn8sg4yQ5nTBZe8AJXF8Mn/2R2NUqqT8OlEEBFkNRktbO8th+rr0h9O/j9Y/y5s+sjuaJRSnYBPJ4Lweo+GOpTxd1lzInx4rzViqlJKHQefTgQR9SqLOxT/AGu46pIs+KKdDIuhlOqwfDoRuAP88HcI+R0tEYA1/PVJv4SVr1ijpiqlVAv5dCIQESI6Qu/ixkz6FUT1hgV3WlNiKqVUC/h0IgCr5VBBR6sjqOMMsh4R5e+Er3VgOqVUy/h8IogI6sAlAoCk8ZAyA5Y+A+nL7Y5GKdUBaSJwB3ScfgSNOeNha3rMBXdYcxgopdQx8PlEEN7RSwRgzW085UnI3qQdzZRSx0wTQZCz4/UjaEifM2DifVYrop9etzsapVQHookgyElReTU1te14KOrmmvRrSJoIH90He5ucKloppQ7w+URQN/BchxpmojF+/nDp/8AVDm9dB+WFdkeklOoAfD4RhHfU3sWNCe0KU2fD/jSr8rg9T7qjlGoXfD4RHBiKurMkArCalJ7xEGx4H5a1i6kflFLtmM8ngk5XIqhz0p3Q7zz4/LeQ/qPd0Sil2jGvJQIRmS0iWSLSYK2liFwtImtEZK2IfC8iw7wVS1PCPUNR55d28L4EhxOBi56x+he8PR0KM+2OSCnVTnmzRPASMLmJ/TuAU4wxQ4A/As97MZZGdQ0LBGBPQbkdH+9dQREw7VUoL4AXToPMVXZHpJRqh7yWCIwx3wJ5Tez/3hiz37O6FIj3VixNCXU5iQoOYGduqR0f733dh8GMz8HhhBfPgQ0L7I5IKdXONCsRiMhdIhImlv+JyEoROasV45gBfNLE588UkVQRSc3Ozm7Fj7UkRrvZmduJR+/sOghu/sp6feta+G6WtiZSSh3Q3BLBjcaYQuAsIBK4FnisNQIQkVOxEsEDjR1jjHneGJNijEmJjW39CduTooM7b4mgTkgXuP5DGDwVvvoDzL8Nqivsjkop1Q40NxGI5/Vc4FVjzPp621pMRIYC/wUuNMbkHu/5Wiox2k1mQRkV1TV2hdA2nC649L9WD+TVc+CViyA/3e6olFI2a24iWCEin2Mlgs9EJBSoPZ4PFpEE4F3gWmPM5uM51/FKjHZjDKTn+cDInSIw6QGr01nmSnhyKMy5AjZ/BrWdPBEqpRrk38zjZgDDge3GmFIRiQJuaOoNIjIXmATEiEgG8BDgBDDGPAf8HogGnhERgGpjTEpLLuJ4JUYHA7Azt4Q+XULsCKHtDb4U4kfDipdg5auw+RMI7wkjr4eR10JoN7sjVEq1keYmghOBVcaYEhG5BhgJPNnUG4wxVx5l/03ATc38fK9K8iSCtM5eT3C4iAQ4/ffWlJebPoIVL8LCP8Giv0DyREiaAIkTrPmR/QPtjlYp5SXNTQTPAsM8nb7uw3qu/wpwircCa0uRbiehgf7s6swth5ri54RBF1lL7jZY+TJs+QK+/pO1399llR6SJkLv0yA+xXrEpJTqFJqbCKqNMUZELgSeMsb8T0RmeDOwtiQiJMa4fa9E0JDo3nDmI9ZSkgu7voe0JbBzsVVSWPRniO1vPUIadgW4o+yOWCl1nJqbCIpE5FdYzUYniogDz/P+ziIxOpj1uwvsDqN9CY6GARdYC0DZftj4Aax4GT77FXz5EAyYAqOut0oLWkpQqkNqbquhaUAFVn+CvVi9gB/3WlQ2SIxyk7G/jOqa42oM1bkFRcLI66zOabcsgVHTYesX8PIF8NRo2Pql3REqpVqgWYnA8+X/OhAuIucD5caYV7waWRtLig6mutaQmd8Jxxzyhm6D4dzH4b6f4eL/WNteuxTeuRmKW7/3t1LKe5o7xMTlwI/AZcDlwDIRmerNwNpaYrQbgDRfrTBuKWeQVVdw6xI45UFY/x48PdqaN1mHsVCqQ2juo6HfAKONMdcbY64DxgC/815Yba9+XwLVAv6BcOqv4JbFENMP3r8NXplitUJSSrVrzU0EDmNMVr313GN4b4fQJTQQl9PR+ccc8rYu/eGGT+D8f1rDXj97Evz4gpYOlGrHmvtl/qmIfCYi00VkOvAR8LH3wmp7DoeQGBWsTUhbg8MBKTfC7T9C8snw8f3w9vXWvAhKqXanuZXF/w9r4pihnuV5Y0yjo4V2VJ1+OOq2FtYdrnwTzvwjbPwQnpsIu1faHZVS6jDNfrxjjHnHGHOvZ3nPm0HZJTHaza68Umpr9TFGq3E4YPydcOOn1qB2/zsLlj6rj4qUakeaTAQiUiQihQ0sRSJS2FZBtpXE6GAqqmvZV6RNSFtdzzFwy3fQ5wz49EF48xqrg5pSynZNJgJjTKgxJqyBJdQYE9ZWQbaVA4PP5Wg9gVe4o+DKuXD2n2Hzp9ajol3L7I5KKZ/XqVr+HK+6vgRaT+BFInDi7XDj5yAOax7lb/+ucyEoZSNNBPV0D3fh9BN25mmJwOviR1mPigZeCF//EV69GIr22h2VUj5JE0E9/n4OekZqy6E24wq3Zkqb8m9I/xGeHQ9bdLwipdqa1xKBiMwWkSwRWdfIfhGRf4nIVhFZIyIjvRXLsUiIdmsdQVsSsQaym7kIQrrA65fCh/dA/i67I1PKZ3izRPASMLmJ/ecAfT3LTKzJb2yXFB3MrrxSjDZvbFtd+sPNX8OYX8DKV+BfI+DdX8C+DXZHplSn57VEYIz5Fshr4pALgVeMZSkQISLdvRVPcyVGuymuqCa3pNLuUHyPMwjO/RvctRrGzISNC+DZE2HOFbBrqd3RKdVp2VlHEAek11vP8Gw7gojMFJFUEUnNzvbuEMfacqgdCI+HyX+Be9bDpF9D+jKYfTb89wxY/j8ober+Qil1rDpEZbEx5nljTIoxJiU2Ntarn3VwFFKtJ7CdOwomPQD3rIPJj0F5IXx0L/y9L8yZBmvnQaX+Oyl1vJo7VaU37AZ61luP92yzVXxkEA5BB59rTwKCYdytMPYW2LsW1r4Fa9+xOqU5g62pNFNutHov63SZSh0zOxPBAuAOEXkDGAsUGGP22BgPAIH+fvSICNJHQ+2RCHQfai1n/AF2fm8lhfXzYc0b0H2YVbcw+FKrvkEp1SzebD46F/gB6CciGSIyQ0RuEZFbPId8DGwHtgIvALd5K5ZjlRjt1hJBe+fwg+SJVh+EezfCebOguhLevx1mDYQvHtImqEo1k9dKBMaYK4+y3wC3e+vzj0didDCfrLW9cKKaKzAERs+wHg+lLYYf/wPf/wuWPAkRCRDdG6J6QVRvz8+9rQppp8vuyJVqF+x8NNRuJUW72V9aRUFpFeFup93hqOYSsUoJyRMhPx3WvAlZGyFvG6x5GyoOmxjHFQ4hXQ8uod0gMOywegbPzwFu6D4cegy36iyU6kQ0ETQgIcrTciivhKHuCJujUS0S0RNOvv/gujFWs9O8bdY8yoW7oXiftRTtg92p1mt1WdPnFT/oOhDiUiA+BeJHQ8wJWkmtOjRNBA1IiqnrS1DK0HhNBJ2CCARHW0vPMQ0fYwyY2kPX65TlWbOr7U6FjFRY9y6seNHaFxxrTclZt0Qma2JQHYomggYkRGmnMp8kYt3xNySkC/SbbC0AtbWQu9Xq7Jb2Hez4Fta9Y+0L72klhMTxkDQeIhI1Mah2TRNBA9wB/nQJDdSWQ6pxDgfEnmAtI6+1Sg+5W2HHN1ZS+PkTWPW6dWxYvJUQEsdD0gSr4loTg2pHNBE0Iik6WEsEqvlEIKavtYy+ySoxZG+EtCWwczFs+9qqvAYIi4Pep0Lv0yB5kvW4SikbaSJoRGK0m282e3dcI9WJORzQdZC1jJ1plRhyNlvNW3d8Axs/gJ9eA8RqidTrVOh7JvQcZ71XqTakiaARidFusooqKK2sxh2gvyZ1nEQgtp+1jJ5hTc2Z+ZNVUtj2tdXnYfEsCO0OAy+CQRdbLZI0KbRfxkD+TqvxwO6VYGogMungEpFoNTvuAPQbrhH1B58b0D3M5mhUp+Pw8zQ/TYFT/g/KC2DLF7D+PUidDcueteoWBl0EA6ZYpQb/QLuj9l011VCwC3K2Wgl89wprKc2x9vu7wOEPlcWHvi+4i5X8E0+y6ofiR7fL4U80ETQiSROBakuucBgy1VrKC63K5vXvwbL/wA9PgV8AdBsCcaMOLlG9tcRQX3E27E+DwgwozPQsu63XimIrkfq7PK+BB9edQdbghc4gz+K2vtTzd1p9TnK3WuetrfJ8kKd0d8Jka+7tuFHQZaD1ntI869j9O6zX/J2wZw18+zh881fr3zFulNVwIGGcVWoI62H1jreRdLSZuFJSUkxqaqrXP6egrIphf/icmyYkc9XYBEJc/oQGOnE5HYi2+FBtpSwfti/y3IGutO5GqzyNGALDrKaqod0grLv1WCm0m/UakWD1Z7D5C8ZrirMgcxXsWWX9TjJXQVHmocf4B0F4nOeLNgyqK6C6/LDXMqgqh6oy6/davx+Jv8szLEkviO5zcIiSroPBdYw3h+UF1uRKaYutZc9q61FSncBwK86wHta/n8NhNTgwNdZjROP5ud95MPSyFv3KRGSFMSalwX2aCBo39s9fsq+w4pBtfg4hJNCf+MggxvWK5sRe0YzpFUWY69ChKGpqDRsyC1m8NYclW3NYu7uAqOAAuoe76BERRI9wF90jgugW7sLpcFBVW0tNjaG6tpbqWkNNrSElKYq4iPZXjFQ2qq2B7J+txLBntXW3W7THWor3HfpFBlZnt8hkiEq2XoNjrMdS4mfdwTo8r6YWKoqgssR6vFFZYq1XlUJNpfVopLYKaqqgtto6PjjWGrMpIsFKSBE9rXVXxLE3j62u9Ny974aC3VCQDkV7oaLQupuvKPTEV2wlx7pHMoj1Jd1juDUESMwJB79QgyKPLQ5jrOurKrFe3THeK3FVFB3896srtdT9XLQPMCAOz7+T51UcMGo6nHRHiz5SE0EL7cotZUtWEcUV1RSVV1NcUU1xeTVF5VVs3jt8bzgAABkZSURBVFfMil37qayuxSEwOC6cE3tF0z3cxbIdeXy/LZeCMqso2b9bKCMSIigsryYzv4w9+eVkFZVTe5RffXiQkxeuS2FMclQbXK3q8GproCTb+kLJ3wl5O6xHFHmepXA30Mz/785gqzThDLIeZzic4OdZHE7rC7Z4HxRkWHfXh5CDj2CcQQcfvzj8PHe5nrtbU2vFXFVq3eEfHpsr3EoqgaHWEhBy8OeYE6wv/25Dj/3u3EdpIvCS8qoaftqVzw/bc1m6LZef0vdTVWPoEe5ifJ8YJvSN4cTe0XQJPXKUy6qaWvYVlrOv0EoIfg7B6XDg7yf4O4SSyhrufWsVGXll/OPyYVwwrIcNV6g6lapy607U1Fh39bU1B+/uEeuLPyDYSgLNvRM2xko+BenWQH8F6VYdR3WZ9eilqsxKFFVl1uc5PHe24jj4s7/LKkmExVmPcsJ7Wnf0Orhfq9JE0EbKKmvILakgLiKoVeoR8ksrmfnqCn7ckccDk/tzyym9tH5CKdUiTSUCrzY5EJHJIvKziGwVkQcb2J8gIgtF5CcRWSMi53ozHm8LCvAjPtLdal/WEe4AXp0xhguG9eCvn27it/PXUV1Te8RxO3NLeH3ZTp76egs1R3vepJRSh/Fa81ER8QOeBs4EMoDlIrLAGLOh3mG/Bd4yxjwrIgOxZi1L8lZMHVGgvx9PThtOfGQQzy7aRmZ+GY9cOJjVGfks2ZrD4q05pOcdHDo5JNCf6eOTbYxYKdXReLMfwRhgqzFmO4BnbuILgfqJwAB1NT3hwGHtvxSAwyE8MLk/8ZFB/G7+Oib+bSEAoS5/TuwVzc0Te3FS7xj+8MF6Hv/sZ84a1I0e2tpIKdVM3kwEcUB6vfUMrEnq63sY+FxEfgkEA2c0dCIRmQnMBEhISGj1QDuKq8cm0ic2hJ/S8xmbHMWQuHD8/Q4+3Xv0oiGc9cQ3/P799bxw3SitT1BKNYvd3RKvBF4yxsQD5wKvisgRMRljnjfGpBhjUmJjY9s8yPZkbK9objmlNyMSIg9JAgAJ0W7uPuMEvty4j8/W77UpQqVUR+PNRLAb6FlvPd6zrb4ZwFsAxpgfABcQ48WYOr0ZE5IZ0D2Mhxasp7C86uhvUEr5PG8mguVAXxFJFpEA4ApgwWHH7AJOBxCRAViJQMd+Pg5OPwePXTKErKIKHv/0Z7vDUUp1AF5LBMaYauAO4DNgI1broPUi8oiITPEcdh9ws4isBuYC001H69jQDg3rGcH1Jybx2rKdrNi53+5wlFLtnHYo66SKK6o5c9Y3hLmcfPDLCQT4210dpJSyk20dypR9QgL9+eOFg/l5XxEvfLfd7nCUUu2YJoJO7IyBXTl3SDee/GoL27KLj/4GpZRP0kTQyT18wSDcAX7c/cYqKquPHJ5CKaU0EXRyXcJcPHbJUNbuLuAfX2grIqXUkTQR+IDJg7tx5ZgEnv92O99vzTn6G5RSPkUTgY/43fkDSI4J5p63VrG/pNLucJRS7YgmAh/hDvDnX1eMIK+kkgfeWUNHazaslPIeTQQ+ZHBcOP/v7H58vmEfc39MP/oblFI+QROBj7lpQi8m9InhkQ/XszVLm5QqpTQR+ByHQ/jH5cMIcvpx1xs/UVFdY3dISimbaSLwQV3DXPxt6jDWZxYy/rGF3P3GT7yVmk5mftnR36yU6nS8OTGNasfOHNiV564Zxcdr97B4aw7zV1mTw/WKCeakPtFcMTqBwXHhNkeplGoLOuicwhjDz/uKWLwlh++35bJ0ey7GwOzpozmxd7Td4SmlWkFTg85pIlBHyC6q4KoXlpK+v5TZ14/mpD46V5BSHZ2OPqqOSWxoIHNnjiMhys2NLy9nifZGVqpT00SgGhQTEsjcm8eRGBXMjS8tZ/EWTQZKdVZeTQQiMllEfhaRrSLyYCPHXC4iG0RkvYjM8WY86thEhwQy5+axJMcEM+Pl5Xy3RWcRVaoz8loiEBE/4GngHGAgcKWIDDzsmL7Ar4DxxphBwN3eike1jJUMxpEcE8xNL6fy7WZNBkp1Nt4sEYwBthpjthtjKoE3gAsPO+Zm4GljzH4AY0yWF+NRLRQVHMDcm8fRKzaEm15J5fP1e+0OSSnViryZCOKA+gPaZHi21XcCcIKILBGRpSIyuaETichMEUkVkdTsbL0jtUNkcABzbx7LwO5h3Pr6St5ZkWF3SEqpVmJ3ZbE/0BeYBFwJvCAiEYcfZIx53hiTYoxJiY2NbeMQVZ0IdwCv3zSWcb2iuO/t1by0ZIfdISmlWoE3E8FuoGe99XjPtvoygAXGmCpjzA5gM1ZiUO1UcKA/s6eP5uxBXXn4gw08+eUWHdJaqQ7Om4lgOdBXRJJFJAC4Alhw2DHzsUoDiEgM1qOi7V6MSbWCQH8/nr5qJJeOjOefX27mjx9upLZWk4FSHZXXxhoyxlSLyB3AZ4AfMNsYs15EHgFSjTELPPvOEpENQA3w/4wxud6KSbUefz8Hj08dSliQP7OX7KCwvIq/XDIEp5/dTxuVUsdKh5hQx8UYw5NfbeGJL7cwJjmKZ64eSUxIoN1hKaUOo0NMKK8REe4+4wSevGI4azLyueDfi1mTkW93WEqpY6CJQLWKC4fHMe+Wk3CIMPW5H5inzUuV6jA0EahWMzgunA9+OYFRCZHc//ZqHl6wnqqaWrvDUkodhSYC1aqiggN4dcYYbhyfzEvfp3HNf5ext6Dc7rCUUk3QRKBanb+fg99fMJBZlw9jdUY+p/9jEbMX76BaSwdKtUuaCJTXXDIyns/vPoXRyVE88uEGLnx6CavStSJZqfZGE4HyqoRoNy9OH82zV48kp7iCi59Zwu/mr6OgrMru0JRSHjp5vfI6EeGcId2Z0DeGf36xhZe+38En6/Zy/tDuxEcGERcRRHykm7jIICLdTkTE7pCV8imaCFSbCXU5+f0FA7lkZByPfrSReSsyKK6oPuQYd4AfCVFuesUGkxwTTK+YEHrFWq/hbuchxxpjqKk1VNbUkl1UQVZRBVmFFewrLCerqIL9JZUkxQQzKjGSofHhuJx+bXm5SnUY2rNY2cYYQ2FZNen7S9mdX8bu/WVk7C9jZ24JO3JK2JlXSk29MYyCnH7UGkOtJwE0NbyRv0MID3KSW1IJgNNPGNgjnFEJkYxKjOSUfrGEBOp9kPIdTfUs1v8JyjYiQrjbSbg7nMFx4Ufsr6qpZVdeKTuyS9ieU0xWYQV+Djl0EcHp7yAmJJAuoYF0CQukS6iLiCAnDoeQW1zByl35rNy1nxU79/P6sp3MXrKDhCg3z183iv7dwmy4cqXaFy0RKJ9SWV3L0u253P/2aoorqvn7ZcM4d0j3Fp0rq7CcBaszyS6uoKC0ivzSKvLLKskvraK0soYZE5K5/qSk1r0ApVqoqRKBJgLlk/YVlnPraytYuSuf2yb15r6z+uHnaF4ldWZ+Gf/5Zhtzl6dTWV1LgL+DSLeT8CAnEUEBhLud5JVUsmLnfn55Wh/uPfMErQBXttNHQ0odpmuYi7kzx/Hwgg08s2gbG/YU8uS0EUdUSNeXnlfKs99s4+3UdIyBqaPiuXVSbxKjg484tqbW8Jv31vLvr7eSW1LJHy8c3OxEU191TS3bskvo1y30mN+rVHNpIlA+K9Dfj79cMoQhceE8tGAdU55ezG/OHYC/n1BZXUtFdS2V1bVU1tSyalc+7/20G4cI00b35JZTehMf6W703H4O4S+XDCEyOIBnF22joLSKWdOGEejf/JZLxhjuf3s181dlcvGIOB6eMojwoMYTlVItpYlA+byrxibQr1sIt7y2kpmvrmjwmEB/B9eMS+QXp/Sie3hQs84rIjwwuT9R7gAe/XgjheVVPHfNKIKb2Vrphe+2M39VJhP7xrBgdSZLt+fy+NRhTOgb0+xrO5rC8ip+/e5atmeX8NYtJ2pLKh/l1ToCEZkMPIk1Q9l/jTGPNXLcpcA8YLQxpskKAK0jUN5SUFrFz/uKCPB3EODnIMDfQaC/A6efg7Agf9wBLf+SfDs1nQffXcvguHBemj6ayOCAJo//ZnM2N7z4I5MHd+Ppq0aydncB97y5im3ZJUw/KYkHJvcnKOD4+kWszSjg9jkr2Z1fRq0xXDE6gb9cMuS4zult1TW1vJmazvjeMSTFHPlITjXOlspiEfHDmoz+TKxJ6pcDVxpjNhx2XCjwERAA3KGJQHVWn6/fyx1zf6JLaCBPXTWS4T0jGjwuLaeEKU8tpkdEEO/cetKBEkR5VQ1//XQTLy5Jo1dMMLOmDW/0HE0xxvDq0p386cONxIQE8O+rRvD5hn3855vtvHTDaCb163Jc1+ktVTW13P3GKj5au4cgpx+/O38gV47pqRXxzWTXDGVjgK3GmO3GmErgDeDCBo77I/BXQMcqVp3aWYO68cbMcVZF87Pf88K326k9rFdccUU1N7+SisMhvHBdyiGPkVxOPx66YBBzbhpLeVUNlzyzhJmvpLJwU9YhHe+aUlhexR1zfuL3769nfJ9oPrpzIqMSo7jnjBPo2yWEB99ZS0Fp+xsHqqK6htteX8lHa/dw1+l9GZUYya/fW8tNL6eSXVRhd3gdnjcTQRyQXm89w7PtABEZCfQ0xnzU1IlEZKaIpIpIanZ2dutHqlQbGZkQycd3TuSMAV159OONzHh5ObnF1hdZba3h3jdXsT2nhKevGknPqIYro0/qE8On95zMzSf3YsXO/dzw0nIm/PVrZn2xmYz9pUccX15Vw7bsYj5fv5cL/r2YT9fv5cFz+vO/6w8+onI5/Zh1+XCyiyv4wwfrvfcLaIHyqhp+8eoKvtiwj0cuHMQ9Z57AKzeO4ffnD+S7rTlMfuJbvtywz+4wj2CM4fttOcz/aTflVTV2h9Mkbz4amgpMNsbc5Fm/FhhrjLnDs+4AvgamG2PSRGQRcL8+GlK+wBjDa0t38scPNxIZ7OTJK0awdHsuT3y5hd+dP5AZE5KbdZ7K6lq+3LiPN5an890W6yZpQp8YwoOcZHiG7MgpPnjH3C3MxVNXjSAlKarB8836YjP/+moL/7l2FGcP6tbsa8kuqmBrVjHbckooKK2kwtPqqryqhoqqWiqqa+gZ5ebqsYl0C3c167wAZZU13PxKKku25fDni4dw5ZiEQ/b/vLeIu99cxcY9hVw5pie/OW+gVyq8M/PLWLA6k6827qNv11CuGpPQYG94qEsAuTzx5WaWp+0HoEtoIDNP7sVVYxOOq67peNhVR3Ai8LAx5mzP+q8AjDF/8ayHA9uAYs9bugF5wJSmkoEmAtWZrM8s4JdzfiItt4RaA5eOjOfvlw1t0XPv9LxS3l6RwfyfduMQrBFdI4KIjwwiPiqIuAg3g3qENdlqqbK6loufWcK+wnI+v+cUohqo1M4pruCD1Zls3FPI1qxitmYVU1h+6OCBIuDy9yPQ6cDl70eAv4OM/aU4RDhvaHduHJ/MsKPUb5RUVHPjS8tZnpbH36YOY+qo+AaPq6iuYdYXm3n+2+24nX6cO6Q7l4yMZ2xyFI5G+m5UVNewalc+O3NL6RruOjAKbv2BCfeXVPLxuj28vyqTH3fkATCgexg7coopr6plcFwYV45JYMqwHoS6nBhj+GGblcx/TMujW5iL207tTXJMMM8s3MYP23OJCg5gxoRkrj0xkTBX85sC19QalmzNoXu4i75dW9anxK5E4I9VWXw6sBursvgqY0yD5U4tEShfVVJRzSMfbCC7uIJnrh5p+yipm/YWcsG/F3PmwK48fdXIA0lpVXo+r3yfxodr9lBZU0tMSAC9Y0Po0yWEvl1C6NMllN5dgokODsTpJ0cks/S8Ul76Po03l6dTXFHNqMRIbhyfzNmDulJeXUtucQU5xZUHXt9ekc6ajAL+OW04U4b1OGrcq9PzeX3ZTj5eu5fiimriI4O4ZEQcl4yMp1u4i1Xp+SzdnsvS7bn8tCufiuojZ8yLCQkkLjKI4AA/lqflUVVj6B0bzEXD45gyvAeJ0cEUlFXx/qrdzFm2i017i3AH+HHekO7szCvlxx15dA0L5LZJfZg2uuch/5apaXk8tXAri37OJtTlzzXjEjmtfxeGxUcQ4N/wU/pduaXMW5HOvBUZZBaUc824BP50Uctadtk2xISInAs8gdV8dLYx5lEReQRINcYsOOzYRWgiUKpdeGbRVv726c88PnUoDhFe+SGN1RkFBAf4MXVUPNeemEifLi27My0qr2LeigxeXJLGrrxS/BzSYGV3kNOPWZcP45xjHAuqrLKGz9bv5Z2VGSzemoMxEODnoLKmFhEY1COMccnRjO0VzQldQ8gqqiBjf+mB0W8z9peRV1LJhL4xXDi8BwO7hzVYQjPGsDqjgLnLdrFgdSahLn9um9SbK8YkNJnM12YU8PTCrXy2YS/GgMvpYGRCJON6RTM2OYr+3cL4+ud9vLU8gx+25yICJ/eN5bKUeM4Y0LXFNwo61pBS6phU19Qy9bkfDkwt2js2mOtPSuLiEXGEHsMjjabU1Bq+2riPFbv2E+UOIDokkJiQAGJCAokOCSA6OLDRO+Xm2ltQzvxVu8krqWRMUhSjk6O80ju7vKoGf4fg79f8ePeXVPJjWh7LtuexdHsuG/cWUv/rOCHKzeUp8VwyMp4eEc3rxNgUTQRKqWO2K7eU/3y7jXOHdOek3tHaXt/LCkqr+DEtj/WZBYzrFc2YpMbrOFpCE4FSSvk4uzqUKaWU6gA0ESillI/TRKCUUj5OE4FSSvk4TQRKKeXjNBEopZSP00SglFI+ThOBUkr5uA7XoUxEsoGdLXx7DJDTiuF0JL567XrdvkWvu3GJxpjYhnZ0uERwPEQktbGedZ2dr167Xrdv0etuGX00pJRSPk4TgVJK+ThfSwTP2x2AjXz12vW6fYtedwv4VB2BUkqpI/laiUAppdRhNBEopZSP85lEICKTReRnEdkqIg/aHY+3iMhsEckSkXX1tkWJyBcissXzGmlnjN4gIj1FZKGIbBCR9SJyl2d7p752EXGJyI8istpz3X/wbE8WkWWev/c3RSTA7li9QUT8ROQnEfnQs97pr1tE0kRkrYisEpFUz7bj+jv3iUQgIn7A08A5wEDgShEZaG9UXvMSMPmwbQ8CXxlj+gJfedY7m2rgPmPMQGAccLvn37izX3sFcJoxZhgwHJgsIuOAvwL/NMb0AfYDM2yM0ZvuAjbWW/eV6z7VGDO8Xt+B4/o794lEAIwBthpjthtjKoE3gAttjskrjDHfAnmHbb4QeNnz88vARW0aVBswxuwxxqz0/FyE9eUQRye/dmMp9qw6PYsBTgPmebZ3uusGEJF44Dzgv551wQeuuxHH9XfuK4kgDkivt57h2eYruhpj9nh+3gt0tTMYbxORJGAEsAwfuHbP45FVQBbwBbANyDfGVHsO6ax/708A/wfUetaj8Y3rNsDnIrJCRGZ6th3X37l/a0an2j9jjBGRTttmWERCgHeAu40xhdZNoqWzXrsxpgYYLiIRwHtAf5tD8joROR/IMsasEJFJdsfTxiYYY3aLSBfgCxHZVH9nS/7OfaVEsBvoWW893rPNV+wTke4Antcsm+PxChFxYiWB140x73o2+8S1Axhj8oGFwIlAhIjU3eh1xr/38cAUEUnDetR7GvAknf+6Mcbs9rxmYSX+MRzn37mvJILlQF9Pi4IA4Apggc0xtaUFwPWen68H3rcxFq/wPB/+H7DRGDOr3q5Ofe0iEuspCSAiQcCZWPUjC4GpnsM63XUbY35ljIk3xiRh/X/+2hhzNZ38ukUkWERC634GzgLWcZx/5z7Ts1hEzsV6pugHzDbGPGpzSF4hInOBSVjD0u4DHgLmA28BCVhDeF9ujDm8QrlDE5EJwHfAWg4+M/41Vj1Bp712ERmKVTnoh3Vj95Yx5hER6YV1pxwF/ARcY4ypsC9S7/E8GrrfGHN+Z79uz/W951n1B+YYYx4VkWiO4+/cZxKBUkqphvnKoyGllFKN0ESglFI+ThOBUkr5OE0ESinl4zQRKKWUj9NEoFQbEpFJdSNlKtVeaCJQSikfp4lAqQaIyDWecf5Xich/PAO7FYvIPz3j/n8lIrGeY4eLyFIRWSMi79WNBS8ifUTkS89cAStFpLfn9CEiMk9ENonI61J/QCSlbKCJQKnDiMgAYBow3hgzHKgBrgaCgVRjzCDgG6xe2wCvAA8YY4Zi9Wyu2/468LRnroCTgLrRIUcAd2PNjdELa9wcpWyjo48qdaTTgVHAcs/NehDWIF61wJueY14D3hWRcCDCGPONZ/vLwNue8WDijDHvARhjygE85/vRGJPhWV8FJAGLvX9ZSjVME4FSRxLgZWPMrw7ZKPK7w45r6fgs9ce+qUH/Hyqb6aMhpY70FTDVM9573XywiVj/X+pGtrwKWGyMKQD2i8hEz/ZrgW88s6RliMhFnnMEioi7Ta9CqWbSOxGlDmOM2SAiv8WaBcoBVAG3AyXAGM++LKx6BLCG/X3O80W/HbjBs/1a4D8i8ojnHJe14WUo1Ww6+qhSzSQixcaYELvjUKq16aMhpZTycVoiUEopH6clAqWU8nGaCJRSysdpIlBKKR+niUAppXycJgKllPJx/x/t/iDWL7D2IwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reload the saved weights of the best model found during the training (according to validation accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(\"logs/best_model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the best model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.8271\n",
      "\tacc: 0.8196\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions for all nodes:\n",
    "\n",
    "Note that the `predict` or `predict_generator` function now operates differently to the `GraphSAGE` or `HinSAGE` models\n",
    "in that if you give it a set of nodes, it will still return predictions for **all** nodes in the graph, and in a fixed order defined by the order of nodes in `X` and `A` (which is defined by the order of `G.nodes()`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = node_data.index\n",
    "all_gen = generator.flow(all_nodes)\n",
    "all_predictions = model.predict_generator(all_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for full-batch methods the batch size is 1 and the predictions have shape $(1, N_{nodes}, N_{classes})$ so we we remove the batch dimension to obtain predictions of shape $(N_{nodes}, N_{classes})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions = all_predictions.squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_predictions = target_encoding.inverse_transform(all_predictions)\n",
    "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31336</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061127</th>\n",
       "      <td>subject=Rule_Learning</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106406</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13195</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37879</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126012</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107140</th>\n",
       "      <td>subject=Case_Based</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102850</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31349</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106418</th>\n",
       "      <td>subject=Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123188</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128990</th>\n",
       "      <td>subject=Genetic_Algorithms</td>\n",
       "      <td>Genetic_Algorithms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109323</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217139</th>\n",
       "      <td>subject=Case_Based</td>\n",
       "      <td>Case_Based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31353</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32083</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126029</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118017</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49482</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753265</th>\n",
       "      <td>subject=Theory</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Predicted                    True\n",
       "31336           subject=Neural_Networks         Neural_Networks\n",
       "1061127           subject=Rule_Learning           Rule_Learning\n",
       "1106406  subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "13195    subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "37879     subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "1126012   subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "1107140              subject=Case_Based                  Theory\n",
       "1102850         subject=Neural_Networks         Neural_Networks\n",
       "31349           subject=Neural_Networks         Neural_Networks\n",
       "1106418                  subject=Theory                  Theory\n",
       "1123188         subject=Neural_Networks         Neural_Networks\n",
       "1128990      subject=Genetic_Algorithms      Genetic_Algorithms\n",
       "109323    subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "217139               subject=Case_Based              Case_Based\n",
       "31353           subject=Neural_Networks         Neural_Networks\n",
       "32083           subject=Neural_Networks         Neural_Networks\n",
       "1126029  subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "1118017         subject=Neural_Networks         Neural_Networks\n",
       "49482           subject=Neural_Networks         Neural_Networks\n",
       "753265                   subject=Theory         Neural_Networks"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data[\"subject\"]})\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node representations\n",
    "\n",
    "Evaluate node representations as activations of the output layer and visualise them, coloring nodes by their true subject label. We expect to see nice clusters of papers in the node representation space, with papers of the same subject belonging to the same cluster.\n",
    "\n",
    "We are going to project the node representations to 2d using either TSNE or PCA transform, and visualise them, coloring nodes by their true subject label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = all_predictions\n",
    "y = np.argmax(\n",
    "    target_encoding.transform(node_data[[\"subject\"]].to_dict(\"records\")), axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=list(G.nodes()))\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=list(G.nodes()))\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of SGC activations for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
